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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93895
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor林致廷zh_TW
dc.contributor.advisorChih-Ting Linen
dc.contributor.author謝尹慈zh_TW
dc.contributor.authorYin-Tzu Hsiehen
dc.date.accessioned2024-08-09T16:15:39Z-
dc.date.available2024-08-10-
dc.date.copyright2024-08-09-
dc.date.issued2024-
dc.date.submitted2024-07-29-
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93895-
dc.description.abstract揭開人體中複雜的神經網絡是理解大腦功能的重要一步。在神經活動的全面記錄中,同時取得大範圍神經群體,並擁有高時空解析度的體積成像技術至關重要,有助於理解神經訊號的傳遞與細胞之間的溝通。在神經科學領域中,雙光子高速鈣離子成像技術,以其亞微米空間解析度在捕捉神經活動中扮演重要角色。然而,由於取像速度與圖像質量之間的平衡,受限於有限的光子訊號流量,總導致低信噪比和對比度差的問題。
為了提升高速體積影像的品質,我們開發了一套深度學習降噪算法(TAG-lens-based SPAtial redundancy-driven noise Reduction Kernel, TAG-SPARK),並將其與具有視頻速率的雙光子體積成像系統整合。利用可調式聲波梯度折射率透鏡(TAG lens)實現高速連續焦點變化,取得垂直(axial)空間冗餘資訊,進行自監督模型訓練的開發。結合此降噪深度學習演算法的影像處理方法,該技術實現了超過300%的峰值信噪比(PSNR)和超過700%的信噪比 (SNR)增強,同時保持神經活動的訊號變化特徵。
為了展示此技術的能力,我們以觀察活體小鼠的Purkinje cells 個體與群體反應為例。Purkinje cell在小鼠運動時的樹突神經訊號表現被視為其關鍵特徵。樹突細胞的鈣離子變化與complex spike之間存在顯著相關性。儘管相關的研究已經證實神經訊號由樹突到細胞本體的傳遞概念具有基礎性意義,但僅限於二維的平面影像研究,仍需要高速體積成像進行完整的驗證。
因此,我們的研究旨在探索神經訊號如何在獨立神經細胞中從樹突到細胞本體的傳遞,以及群體細胞之間的相互溝通。技術不僅以高品質影像捕捉神經活動,還有助於我們更深入地理解三維神經結構中神經訊號的傳遞途徑。
zh_TW
dc.description.abstractComprehensive recording of neural activities across large neuronal populations with high spatiotemporal resolution and volumetric imaging is crucial for understanding neural circuit signal transduction, information processing, and behavior generation. Two-photon high-speed fluorescence calcium imaging stands as a leading technique in neuroscience for capturing neural activities with sub-micrometer spatial resolution. However, challenges arise from the inherent trade-off between acquisition speed and image quality, resulting in a low signal-to-noise ratio (SNR) due to limited signal photon flux.
To enhance the quality of high-speed volumetric imaging, we developed a deep-learning denoising algorithm, TAG-SPARK (TAG-lens-based SPAtial redundancy-driven noise Reduction Kernel). We propose a home-built two-photon volumetric imaging system with video-rate capabilities, integrating image processing methods using a noise reduction deep learning algorithm. Leveraging the high-speed dense z-sampling at sub-micrometer-scale intervals of a TAG lens, we developed a self-supervised denoising algorithm that exploits the spatial redundancy of z-slices. This approach achieves >300% peak signal-to-noise ratio (PSNR) and >700% SNR enhancement while preserving the fast-spiking functional profiles of neuronal activities.
We demonstrated this technology's application by observing individual neurons and populations of Purkinje cells (PCs) in awake mice. PC activities, which induce dendritic calcium spikes, have been identified as a key feature of motor initiation. The correlation between dendritic and somatic complex spikes is significant. While the foundational importance of the concept of neuronal signal transmission from dendrites to soma is acknowledged, validating this requires high-speed volumetric calcium imaging.
Therefore, our study aims to explore how neural signals propagate from dendrites to cell bodies within individual neurons, as well as communication among neuronal populations. This tailored technique enables capturing neuronal activities with high SNR, advancing our understanding of neuronal signal transduction pathways within 3D neuronal architecture.
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dc.description.tableofcontents誌謝 i
中文摘要 iii
ABSTRACT iv
CONTENTS vi
LIST OF FIGURES ix
LIST OF TABLES xii
Chapter 1 Introduction 1
1.1 Introduction of brain, neuron, and neural signals 1
1.2 Common techniques for functional brain study and limitations 3
1.3 Optical microscopy for brain study 5
1.4 High-speed volumetric imaging for brain study 7
1.5 Image processing and deep-learning techniques for image contrast enhancement 9
1.6 Complex mouse brain study - Purkinje cells for example 11
1.7 Aim and structure of this thesis 13
Chapter 2 Principle of the integration of two-photon TAG imaging system and deep-learning denoise method, TAG-SPARK 15
2.1 Two-photon laser scanning microscopy 15
2.2 Tunable Acoustic Gradient Lens (TAG lens) 18
2.3 Introduction of deep-learning model 25
2.3.1 The concept of creating deep-learning models 25
2.3.2 Supervised learning and self-supervised learning 28
2.3.3 Introduction of U-net 30
2.3.4 TAG-lens-based SPAtial redundancy-driven noise Reduction Kernel (TAG-SPARK) 32
Chapter 3 Optical system design and sample preparation 35
3.1 Optical setup 35
3.2 Sample preparation 38
3.2.1 Standard sample preparation - fluorescent microspheres 38
3.2.2 Mouse preparation 39
3.3 Theoretical and experimental performance 40
3.3.1 Spatial resolution 40
3.3.2 Field of view (FOV) 44
3.3.3 Extension depth of field (DOF) 47
3.3.4 Imaging volume rate 62
Chapter 4 Image processing and analysis 64
4.1 Image processing 64
4.1.1 High-speed volumetric image reconstruction 64
4.1.2 Image processing: Brightness adjustment, deconvolution, and motion correction 70
4.1.3 Automated image segmentation and labeling 75
4.1.4 Appendix: Identification of individual synapses in Drosophila antennal lobe by machine learning-based super-resolution image analysis 79
4.2 Image analysis 82
4.2.1 Clustering analysis: PCA, k-means 82
4.2.2 Quantify image enhancement: SNR, PSNR, PCC 86
4.2.3 Statistics analysis: Z-test, T-test and ANOVA analysis 89
4.3 Parameter setting for deep-learning denoise model: TAG-SPARK 94
Chapter 5 Results and analysis 98
5.1 High-speed volumetric image related to in vivo mouse behavior recording 98
5.2 Evaluation of functional images: comparing before and after 104
5.2.1 Pre-image processing for analysis 104
5.2.2 Evaluation of denoising method: TAG-SPARK for structural similarity and SNR optimization 105
5.3 Individual neural signal transmission and population neural behavior 110
5.3.1 Signals transmission through individual neurons 110
5.3.2 Relation between large cell populations and depth of brain 113
Chapter 6 Discussion 117
REFERENCE 128
FOOTNOTE 136
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dc.language.isoen-
dc.titleTAG-SPARK: 深度學習提升高速雙光子體積成像系統應用於活體老鼠小腦運動神經之研究zh_TW
dc.titleTAG-SPARK: Empowering High-Speed Volumetric Imaging with Deep-Learning and Spatial Redundancy for the Cerebellar Neuronal Activitiesen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.coadvisor朱士維zh_TW
dc.contributor.coadvisorShi-Wei Chuen
dc.contributor.oralexamcommittee潘明楷;吳順吉;陳壁彰zh_TW
dc.contributor.oralexamcommitteeMing-Kai Pan ;Shun-Chi Wu ;Bi-Chang Chenen
dc.subject.keyword多光子顯微鏡,可調式聲波梯度折射率透鏡,高速體積成像,深度學習降噪演算法,空間冗餘資訊,神經網絡,Purkinje cell,zh_TW
dc.subject.keywordMulti-photon microscopy,Tunable acoustic gradient index lens,High-speed volumetric imaging,Deep-learning noise reduction,Spatial redundancy,Neural networks,Purkinje cells,en
dc.relation.page137-
dc.identifier.doi10.6342/NTU202402518-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2024-08-01-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept電子工程學研究所-
顯示於系所單位:電子工程學研究所

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